Interpretation of gascromatographic data via artificial neural networks for the classification of marine bacteria

Ruggiero, Carmelina; Calegari, F; Berti, R; Bertone, Stefania; Casareto, L

doi:10.1007/bf00746063

Cited by 3 publications

(1 citation statement)

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“…The first FAMEbased classification of bacteria by means of artificial neural networks (ANN) was oriented toward seven genera of several marine bacteria. The scope of this experiment was rather small as the genera were represented by only 36 strains (Ruggiero et al 1993). Extension of this research was done by classifying 4, 5, and 14 genera of marine and environmental bacteria respectively, covered by 35, 26, and 39 species, and 71, 50, and 45 strains (Bertone et al 1996;Giacomini et al 2000;Giacomini et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Genus-wide Bacillus species identification through proper artificial neural network experiments on fatty acid profiles

Slabbinck

Baets

Dawyndt

et al. 2008

Antonie van Leeuwenhoek

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Gas chromatographic fatty acid methyl ester analysis of bacteria is an easy, cheap and fast-automated identification tool routinely used in microbiological research. This paper reports on the application of artificial neural networks for genus-wide FAME-based identification of Bacillus species. Using 1,071 FAME profiles covering a genus-wide spectrum of 477 strains and 82 species, different balanced and imbalanced data sets have been created according to different validation methods and model parameters. Following training and validation, each classifier was evaluated on its ability to identify the profiles of a test set. Comparison of the classifiers showed a good identification rate favoring the imbalanced data sets. The presence of the Bacillus cereus and Bacillus subtilis groups made clear that it is of great importance to take into account the limitations of FAME analysis resolution for the construction of identification models. Indeed, as members of such a group cannot easily be distinguished from one another based upon FAME data alone, identification models built upon this data can neither be successful at keeping them apart. Comparison of the different experimental setups ultimately led to a few general recommendations. With respect to the routinely used commercial Sherlock Microbial Identification System (MIS, Microbial ID, Inc. (MIDI), Newark, Delaware, USA), the artificial neural network test results showed a significant improvement in Bacillus species identification. These results indicate that machine learning techniques such as artificial neural networks are most promising tools for FAME-based classification and identification of bacterial species.

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Section: Introductionmentioning

confidence: 99%